Promoter region-based classification of genes

Proceedings of the Pacific Symposium on Biocomputing, January
3-7, 2001. pp. 151-163.

Abstract

In this paper we consider the problem of extracting information from
the upstream untranslated regions of genes to make predictions about
their transcriptional regulation. We present a method for classifying
genes based on motif-based hidden Markov models (HMMs) of their
promoter regions. Sequence motifs discovered in yeast promoters are
used to construct HMMs that include parameters describing the number
and relative locations of motifs within each sequence. Each model
provides a Fisher kernel for a support vector machine, which can be
used to predict the classifications of unannotated promoters. We
demonstrate this method on two classes of genes from the budding
yeast, S. cerevisiae. Our results suggest that the additional
sequence features captured by the HMM assist in correctly classifying
promoters.
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